Maintenance scheduling and production control of multiple-machine manufacturing systems

This paper deals with the production and preventive maintenance control problem for a multiple-machine manufacturing system. The objective of such a problem is to find the production and preventive maintenance rates for the machines so as to minimize the total cost of inventory/backlog, repair and preventive maintenance. A two-level hierarchical control model is presented, and the structure of the control policy for both identical and non-identical manufacturing systems is described using parameters, referred to here as input factors. By combining analytical formalism with simulation-based statistical tools such as experimental design and response surface methodology, an approximation of the optimal control policies and values of input factors are determined. The results obtained extend those available in existing literature to cover non-identical machine manufacturing systems. A numerical example and a sensitivity analysis are presented in order to illustrate the robustness of the proposed approach. The extension of the proposed production and preventive maintenance policies to cover large systems (multiple machines, multiple products) is discussed.     

Information field in a manufacturing System: Concepts,measurements and applications

As the core of the Industry 4.0 era, information can improve the efficiency of production as well as bring cognitive load to operators. In this study, we aim to develop a unified information field analysis model for manufacturing systems and to estimate the cognitive load of operators through the action features of the information field. The qualitative and quantitative analytical framework model of the information field in manufacturing systems is established using information entropy and fuzzy mathematics. Furthermore, the cognitive load mechanism in manufacturing systems is clarified. The information principles that must be exercised to improve and optimise manufacturing systems under the information field framework are proposed for implementing lean production and digital transformation. Results of a case study show that the proposed information field analysis model for manufacturing systems reveal the change law of information field of manufacturing system in time and space, which has considerable guiding values for enhancing the management efficiency of shop floors and the sustainable development of enterprises.     

Optimal manufacturing–remanufacturing policies in a lean production environment

This study analyses a production-management model that considers the possibility of implementing a reverse-logistics system for remanufacturing end-of-life products in a lean production environment (as opposed to models that use EOQ approaches). Decision variables are identified (including manufacturing and remanufacturing capacities and return rates and use rates for end-of-life products) and optimal policies are determined. Moreover, the structure of these optimal policies is analysed. The conclusion drawn is that, in many realistic scenarios, mixed policies (that is, with return rates and use rates strictly between 0 and 1) can be optimal. This conclusion is contrary to results published in earlier studies, which are based on more restrictive assumptions.     

The impact of OPT and TPM on the economic production quantity

In manufacturing systems, the material flow is influenced by a number of factors, such as batching policies, capacity of machines, machine breakdowns, etc. Realizing the role of batching policies and reliability of machines in production systems, a mathematical model is presented here for determining optimal batching policies with the objective of improving the speed of material flow considering machine breakdowns and batch splitting and forming. This model is employed for studying (i) the significance of total preventive maintenance (TPM); (ii) the use of the optimized production technology (OPT) concept in batching policies; and (iii) the influence of a set-up cost reduction programme on the performance of manufacturing systems. The basic criterion considered for optimizing the batch sizes is the minimization of total system cost (TSC). An example problem is solved to explain the application of the model.     

Optimal threshold control in discrete failure-prone manufacturingsystems

Bielecki and Kumar (1988) show that the threshold or hedging-point production policies are optimal in a continuous manufacturing system, even if production ability is uncertain. Their analysis assumes constant demand and processing time. In this paper, we consider a discrete manufacturing system in which production capacity, demand, and processing time are all nondeterministic. We formulate the problem into a discrete Markovian production model, and explore the most cost-effective control policy for such a system. With two more sources of uncertainty, we find that the threshold control policies are optimal among all feasible policies when the long run average cost is to be minimized. This extends Bielecki and Kumar's result which shows that the threshold policies are optimal among a subset of feasible policies     

An optimal batch size under a periodic delivery policy

This paper addresses the problem of a manufacturing system that procures raw materials from suppliers in a lot and processes them to convert to finished products. It proposes an ordering policy for raw materials to meet the requirements of a production facility. In turn, this facility must deliver finished products demanded by outside buyers at fixed interval points in time. First, a general cost model is developed considering both raw materials and finished products. Then this model is used to develop a simple procedure to determine an optimal ordering policy for procurement of raw materials and also the manufacturing batch size to minimize the total cost for meeting the customer demand on time. The proposed procedure provides better results than the traditional separate policies. Numerical examples are also presented.     

An extended model of design process of lean production systems by means of process variables

In this paper, we present an axiomatic modeling of lean production system design, using process variables (PVs). So far, we had developed a model for conceptual design of lean production systems by means of FR–DP relationships, the key characteristics of axiomatic design (AD) methodology, appeared in the proceedings of Second International Conference of Axiomatic Design. Albeit the model in question was thorough enough to be applied in various cases, its embedded abstract principles hamper straightforward applications and the required resources, tools, and techniques are not clarified. In AD terms, it lacks PVs created by mapping the design parameters (DPs) to the process domain to clarify the means that produce the specified DPs. Owing to the difficulties involved in the definition of PVs for manufacturing systems, there is few works in this area. This paper is an attempt to introduce PVs in production system design. When we are developing a product (i.e. a part), we can simply interpret the set of PVs as the process design. In the case of a production system we interpret PVs as the tools, methods, and resources, required for implementing a lean production system. In this paper, according to AD methodology, we have developed a hierarchical structure to model the design process of a lean production system, composed of FRs, DPs, and PVs. Serving as an efficient guideline for the design process and clarifying the required tools, methods, and resources, this structure is general enough to be applied for different cases.     

An optimal batch size for a JIT manufacturing system

This paper addresses the problem of a manufacturing system that procures raw materials from suppliers in a lot and processes them to convert to finished products. It proposes an ordering policy for raw materials to meet the requirements of a production facility. In turn, this facility must deliver finished products demanded by outside buyers at fixed interval points in time. In this paper, first we estimate production batch sizes for a JIT delivery system and then we incorporate a JIT raw material supply system. A simple algorithm is developed to compute the batch sizes for both manufacturing and raw material purchasing policies. Computational experiences of the problem are also briefly discussed.     

An optimal batch size for a production system operating under periodic delivery policy

A manufacturing system that procures raw materials from suppliers in a lot and processes them into a finished product is considered in this research. An ordering policy is proposed for raw materials to meet the requirements of a production facility which, in turn, must deliver finished products demanded by outside buyers at fixed points in time. First, a general cost model is formulated considering both raw materials and finished products. Then, using this model, a simple procedure is developed to determine an optimal ordering policy for procurement of raw materials, and the manufacturing batch size, to minimize the total cost of meeting customer demands in time. The dependent relationships between production batch size and rawmaterial purchasing quantity, and various delivery patterns considered in recent literature are critically reviewed. The quality of the solution is evaluated. Numerical examples are provided.     

Mathematical programming representation of pull controlled single-product serial manufacturing systems

Pull policies may perform quite differently depending on the particular manufacturing system they must control. Hence, it is clear the necessity of having efficient performance evaluation models to select the best control policy in a specific context. This paper proposes a mathematical programming representation of the main pull control policies applied to single-product serial manufacturing systems. The proposed models simulate the pull controlled system in the sense that, if instantiated with the same parameter values as in a simulation model, their solution gives the same event sequence of the simulation. The proposed mathematical representation is also used for a formal comparison of the considered pull control policies. The new models presented in this paper can represent a base to build new efficient optimization algorithms for the design of pull controlled production systems.     

Equipment replacement decisions and lean manufacturing

Traditional manufacturing systems are built on the principle of economies of scale. Here, the large fixed costs of production are depreciation-intensive because of huge capital investments made in high-volume operations. These fixed costs are spread over large production batch sizes in an effort to minimize the total unit costs of owning and operating the manufacturing system. As an alternative to “batch-and-queue,” high-volume, and inflexible operations, the principles of the Toyota Production System (TPS) and lean manufacturing have been widely adopted in recent years in the US [1, 2, 3 and 4]. In this paper, we illustrate an equipment replacement decision problem within the context of lean manufacturing implementation. In particular, we demonstrate how the value stream mapping (VSM) suite of tools can be used to map the current state of a production line and design a desired future state. Further, we provide a roadmap for how VSM can provide necessary information for analysis of equipment replacement decision problems encountered in lean manufacturing implementation.     

The robustness of scheduling policies in multi-product manufacturing systems with sequence-dependent setup times and finite buffers

In this paper, a continuous time Markov chain model is introduced to study multi-product manufacturing systems with sequence-dependent setup times and finite buffers under seven scheduling policies, i.e., cyclic, shortest queue, shortest processing time, shortest overall time (including setup time and processing times), longest queue, longest processing time, and longest overall time. In manufacturing environments, optimal solution may not be applicable due to uncertainty and variation in system parameters. Therefore, in this paper, in addition to comparing the system throughput under different policies, we introduce the notion of robustness of scheduling policies. Specifically, a policy that can deliver good and stable performance resilient to variations in system parameters (such as buffer sizes, processing rates, and setup times) is viewed as a “robust” policy. Numerical studies indicate that the cyclic and longest queue policies exhibit robustness in subject to parameter changes. This could provide production engineers a guideline in operation management.     

Quantitative lifecycle risk analysis of the development of a just-in-time transportation network system

The automotive manufacturing industry is under financial pressure due to massive cost structure, relatively small scale operation and strong global competition. In order to improve their operational cost efficiency, companies have adopt lean principles in all their manufacturing activities, in particular, just-in-time supply chain. However, a consequence of this policy makes the transportation network from the local supply chain time critical. This paper uses an enterprise system model integrated with a quantitative method to study a manufacturing company’s logistics system re-development project. The quantitative risk analysis examines the project’s systems engineering management plan to see if it is sufficiently to mitigate risks in design, monitoring and validation of the project’s lifecycle processes. The computed risk profile shows a trend of decreasing risk and suggests areas of improvement in the systems engineering plan to ensure greater probability of success. The research assumes a single risk profile for the supply chain. Research is continuing in expanding to more accurate risk profile of the project when partners of the supply chain have individual profiles.     

制造系统的递阶滚动优化调度模型

研究的对象是只有一台不可靠（failure-prone)机器的非完全柔性制造系统，该系统能生产多种产品，但在同一时刻只能生产一种产品，并且当机器由生产一种产品向生产另一种产品切换时，需要考虑setup时间及其成本，待决策变量是setup序列及产品生产率，本文基于非完全柔性制造系统的特点，引入递阶层控的思想，采用新的递阶结构框架和阈值控制策略，对问题进行分解，建立了考虑setup时间及成本的递阶流率控制最优化调度模型，并给出了递阶的滚动优化算法，仿真结果表明，这种调度策略更易于工程实现。     

Robust design of flexible manufacturing systems using, colored Petri net and genetic algorithm

A method is presented for the robust design of flexible manufacturing systems (FMS) that undergo the forecasted product plan variations. The resource allocation and the operation schedule of a FMS are modeled as a colored Petri net and an associated transition firing sequence. The robust design of the colored Petri net model is formulated as a multi-objective optimization problem that simultaneously minimizes the production costs under multiple production plans (batch sizes for all jobs), and the reconfiguration cost due to production plan changes. A genetic algorithm, coupled with the shortest imminent operation time (SIO) dispatching rule, is used to simultaneously find the near-optimal resource allocation and the event-driven schedule of a colored Petri net. The resulting Petri net is then compared with the Petri nets optimized for a particular production plan in order to address the effectiveness of the robustness optimization. The simulation results suggest that the proposed robustness optimization scheme should be considered when the products are moderately different in their job specifications so that optimizing for a particular production plan creates inevitably bottlenecks in product flow and/or deadlock under other production plans.     

A Tractable Class of Maximal Hedging Policies in Multi-Part Manufacturing Systems

The flow control problem in multi-part failure prone manufacturing systems is considered. While computationnaly attractive, the near optimal controllers of Caramanis and Sharifnia, suffer from the drawback that the production capacity set must be approximated via a very restricted set of inscribed hypercubes, namely those for which a componentwise feasibility requirement is satisfied. Also, due to the completely decoupled nature of production along each component, utilization of the restricted capacity set is suboptimal. A class of capacity set incribed hypercube policies called simple maximal hedging (SMH) policies is introduced. In SMH policies production levels along the various components of the capacity set are coupled, the componentwise feasibility requirement is lifted, and there is no underutilization of production capacity if needed. In a p part types manufacturing system, for partwise additive cost functionals, it is shown that performance evaluation of a given SMH policy reduces to the analysis of p decoupled (fictitious) semi-Markovian machines. The machines are Markovianized via first passage-time analysis and a Padé approximants technique. Numerical optimization over the class of SMH policies in a sample manufacturing system indicates that their performance can come close to that of the optimal control.     

Supply-side inoperability input–output model (SIIM) for risk analysis in manufacturing systems

This paper proposes a methodological approach in risk analysis of interdependent manufacturing systems which is based on Ghosh model - a variation of the Leontief input-output model. The proposed approach is able to quantify the impact of supply perturbations in a manufacturing system in terms of cost-price increase in production output due to increase in prices of value-added input brought about by degraded supply resulting from natural or man-made disasters and sudden policy changes. Unlike demand-driven perturbation models presented in literature, the supply-driven inoperability input-output model (SIIM) appears to be more relevant particularly in make-to-order manufacturing systems as demand is usually pre-determined and production costs typically increase when prices of inputs increase. An actual case study was carried out in a furniture manufacturing firm in central Philippines and three scenarios were presented to illustrate the proposed approach: (1) a sudden log ban in the location of the supplier, (2) increase in labor costs and (3) metal shortage caused by severe weather condition. Results show that supply perturbation of upstream processes does not impact downstream processes as long as these processes remain independent of the perturbed upstream process as described in firm's system structure and topology. This also shows that the magnitude of impact of non-perturbed process depends on its nature of interdependence of the perturbed process. The proposed approach is highly relevant for manufacturing practitioners in formulating and implementing mitigation and adaptation policies.     

Neurally-inspired stochastic algorithm for determining multi-stage multi-attribute sampling inspection plans

In manufacturing industries, sampling inspection is a common practice for quality assurance and cost reduction. The basic decisions in sampling inspection are how many manufactured items to be sampled from each lot and how many identified defective items in the sample to accept or reject each lot. Because of the combinatorial nature of alternative solutions on the sample sizes and acceptance criteria, the problem of determining an optimal sampling plan is NP-complete. In this paper, a neurally-inspired approach to generating acceptance sampling inspection plans is proposed. A Bayesian cost model of multi-stage-multi-attribute sampling inspections for quality assurance in serial production systems is formulated. This model can accommodate various dispositions of rejected lott such as scraping and screening. The model also can reflect the relationships between stages and among attributes. To determine the sampling plans based on the formulated model, a neurally-inspired stochastic algorithm is developed. This algorithm simulates the state transition of a primal-dual stochastic neural network to generate the sampling plans. The simulated primal network is responsible for generation of new states whereas the dual network is for recording the generated solutions. Starting with an arbitrary feasible solution, this algorithm is able to converge to a near optimal or an optimal sampling plan with a sequence of monotonically improved solutions. The operating characteristics and performance of the algorithm are demonstratedvia numerical examples.     

A knowledge-based approach to design for manufacturability

In the light of growing global competition, organizations around the world today are constantly under pressure to produce high-quality products at an economical price. The integration of design and manufacturing activities into one common engineering effort has been recognized as a key strategy for survival and growth. Design for manufacturability (DFM) is an approach to design that fosters the simultaneous involvement of product design and process design. The implementation of the DFM approach requires the collaboration of both the design and manufacturing functions within an organization. Many reasons can be cited for the inability to implement the DFM approach effectively, including: lack of interdisciplinary expertise of designers; inflexibility in organizational structure, which hinders interaction between design and manufacturing functions; lack of manufacturing cost information at the design phase; and absence of integrated engineering effort intended to maximize functional and manufacturability objectives. The purpose of this research is to show how expert systems methodology could be used to provide manufacturability expertise during the design phase of a product. An object- and rule-based expert system has been developed that has the capability: (1) to make process selection decisions based on a set of design and production parameters to achieve cost-effective manufacture; and (2) to estimate manufacturing cost based on the identified processes. The expertise for primary process selection is developed for casting and forging processes. The specialized processes considered are die casting, investment casting, sand casting, precision forging, open die forging and conventional die forging. The processes considered for secondary process selection are end milling and drilling. The cost estimation expertise is developed for the die casting process, the milling and drilling operations, and the manual assembly operations. The results obtained from the application of the expert system suggest that the use of expert systems methodology is a feasible method for implementing the DFM approach.     

How do employees perceive their organization and job when companies adopt principles of lean production?

The concept of lean production originally emerged in the beginning of the 1990s in the automobile industry, but its principles were adopted in other industries as well. According to the literature, the principles of lean production are controversial from the point of view of human well being. This study examined: 1) the extent to which lean production and related technologies were adopted in four Finnish manufacturing companies; 2) the way different occupational groups experienced their organization and work when principles of lean production had been implemented; and 3) which factors in the organization and change processes were associated with the employees' positive or negative perceptions of production, job satisfaction, and stress. © 2004 Wiley Periodicals, Inc. Hum Factors Man 14: 157–180, 2004.